Life cycle assessment of an integrated forest biorefinery: hot water extraction process case study
Bibliographic record
Abstract
Abstract The environmental footprint of bioproducts depends on the performance and implementation strategy of the biorefinery processes through which they are produced. Life cycle assessment (LCA) studies are categorized into two general types: attributional and consequential. The consequential life cycle assessment (CLCA) method illustrates the change of flows to and from environment, resulting from different potential decisions. Depending on the analysis goal, CLCA is known to be the proper approach to address the environmental analysis of integrated biorefineries with multiple bioproducts. In this study, an LCA of hot water extraction‐based biorefinery strategy was performed, including five production pathways. Defined process options consisted of an extraction of hemicellulose to produce (i) biogas, (ii) hemicellulose for animal feed, (iii) hemicellulose for C5‐sugars, (iv) C5‐sugars, and (v) furfural. Except for the “biogas”, acetate salt was the by‐product of all the process options. Consequential LCA results proved that the bark consumption, chemicals, and bioproducts transportation have significant environmental impacts. ‘Hemicellulose for C5‐sugars’ and ‘C5‐sugars’ outperformed other alternative options with a greenhouse gas reduction of 80% and 68%, respectively. Also, normalized results of these two options presented remarkable improvement of more than three times in human health impacts in comparison to existing process at the case study mill. © 2015 Society of Chemical Industry and John Wiley & Sons, Ltd
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".